Board independence, CEO duality and firm performance: a quantile regression analysis for Indonesia, Malaysia, South Korea and Thailand

We study the effect of board independence and CEO duality on firm performance for a sample of stock-listed enterprises from Indonesia, Malaysia, South Korea and Thailand, applying Quantile Regression. Quantile Regression is more powerful than standard linear regression, as reflected in the Ordinary Least Square (OLS) regression method, since Quantile Regression can produce estimates for all conditional quantiles of the distribution of a response variable, whereas OLS regression only estimates the conditional mean effects of a response variable. Moreover, Quantile Regression is better able to handle violations of the standard assumptions of normality, homoscedasticity and absence of outliers. Indeed, we find that the relationship between corporate governance and firm performance variables is different across the conditional quantiles of the distribution of firm performance, something OLS would leave unidentified. This finding suggests that estimating the quantile effect of a response variable can well be more insightful than estimating only the mean effect of this response variable, particularly so when the data violate assumptions required to perform OLS regression, as is often the case in corporate governance research.